Nrf2 BL6 PEITC 16S Microbiome Data Visualization

Date: Tue May 28 14:39:12 2019
Scientist: Ran Yin
Sequencing (Waksman): Dibyendu Kumar
Statistics: Davit Sargsyan
Principal Investigator: Ah-Ng Kong

Meta data

# sink(file = "tmp/log_nrf2ubiome_data_visualization_may2019_v1.Rmd.txt")
# date()
options(scipen=999)
require(knitr)

Loading required package: knitr

require(kableExtra)

Loading required package: kableExtra

# # Increase mmemory size to 64 Gb----
# invisible(utils::memory.limit(65536))
options(stringsAsFactors = FALSE)
# str(knitr::opts_chunk$get())
# # NOTE: the below does not work!
# knitr::opts_chunk$set(echo = FALSE, 
#                       message = FALSE,
#                       warning = FALSE,
#                       error = FALSE)
# On Windows set multithread=FALSE----
mt <- TRUE
require(data.table)

Loading required package: data.table
data.table 1.12.2 using 18 threads (see ?getDTthreads).  Latest news: r-datatable.com

require(phyloseq)

Loading required package: phyloseq

require(ggplot2)

Loading required package: ggplot2

require(plotly)

Loading required package: plotly

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

# require(data.table)
require(DT)

Loading required package: DT

require(shiny)

Loading required package: shiny

Attaching package: ‘shiny’

The following objects are masked from ‘package:DT’:

    dataTableOutput, renderDataTable

source("source/functions_may2019.R")
# Load data----
# Counts
load("data_may2019/ps_may2019.RData")
# Taxonomy
load("data_may2019/taxa.RData")
taxa <- data.table(seq16s = rownames(taxa),
                   taxa)
# Samples
samples <- ps_may2019@sam_data
DT::datatable(samples,
              options = list(pageLength = nrow(samples)))

Taxonomic Ranks:

King Phillip Can nOt Find Green Socks * Kingdom
* Phylum
* Class
* Order
* Family
* Genus
* Species

Prune data

Check mapping.

 Bacteria Eukaryota      <NA> 
     7994       116        19 

     Acidobacteria     Actinobacteria      Bacteroidetes      Cyanobacteria 
                 3                177               2102                 31 
   Deferribacteres Epsilonbacteraeota         Firmicutes       Fusobacteria 
                10                 27               4997                  1 
       Parabasalia    Patescibacteria     Planctomycetes     Proteobacteria 
                 1                 63                  1                202 
       Tenericutes    Verrucomicrobia               <NA> 
                84                 66                364 

First, all OTUs that were not bacteria were removed. Also, OTUs that were not mapped to a phylum were removed as these are usually sequencing artifacts.

Bacteria 
    7764 

     Acidobacteria     Actinobacteria      Bacteroidetes      Cyanobacteria 
                 3                177               2102                 31 
   Deferribacteres Epsilonbacteraeota         Firmicutes       Fusobacteria 
                10                 27               4997                  1 
   Patescibacteria     Planctomycetes     Proteobacteria        Tenericutes 
                63                  1                202                 84 
   Verrucomicrobia 
                66 

Richness (Alpha diversity)

OTU table

Total counts per sample (i.e. sequencing depth)

Counts at Phylum level

Relative abundance (%) at Phylum level

Remove phyla with relative abundance of >= 1% in less than 10% of samples (i.e. prevalence >= 10%).

Hence, only 6 out of 13 Phyla were studied in this analysis: Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Tenericutes and Verrucomicrobia. Relative abundance at the next taxonomic level (Class) was, therefore, computed relative to the sum of these 6 Phyla.

[1] "Actinobacteria"  "Bacteroidetes"   "Firmicutes"      "Proteobacteria" 
[5] "Tenericutes"     "Verrucomicrobia"

7,628 OTUs, down from 7,764 OTUs in the previous table.

Relative Abundance in Samples at Different Taxonomic Ranks

1. Class

p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Treatment)) +
  facet_wrap(~ Class,
             scale = "free_y") +
  geom_line() +
  geom_point(aes(shape = Treatment,
                 color = Treatment),
             size = 5,
             alpha = 0.5)
print(p0)

p1 <- ggplot(mu,
             aes(x = x,
                 y = Class,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)")
ggplotly(p1)

p2 <- ggplot(mu,
             aes(x = x,
                 y = Class,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_log10(" Log10 Scale of Relative Abundance (%)")
ggplotly(p2)

Transformation introduced infinite values in continuous x-axis

2. Order

p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Treatment)) +
  facet_wrap(~ Order,
             scale = "free_y") +
  geom_line() +
  geom_point(aes(shape = Treatment,
                 color = Treatment),
             size = 5,
             alpha = 0.5)
print(p0)

p1 <- ggplot(mu,
             aes(x = x,
                 y = Order,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)")
ggplotly(p1)

p2 <- ggplot(mu,
             aes(x = x,
                 y = Order,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_log10(" Log10 Scale of Relative Abundance (%)")
ggplotly(p2)

Transformation introduced infinite values in continuous x-axis

Session Information

sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.5 (Maipo)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] shiny_1.3.2       DT_0.6            plotly_4.9.0      ggplot2_3.1.1    
[5] phyloseq_1.26.1   data.table_1.12.2 kableExtra_1.1.0  knitr_1.23       

loaded via a namespace (and not attached):
 [1] Biobase_2.42.0      httr_1.4.0          tidyr_0.8.3         jsonlite_1.6       
 [5] viridisLite_0.3.0   splines_3.5.0       foreach_1.4.4       assertthat_0.2.1   
 [9] stats4_3.5.0        yaml_2.2.0          pillar_1.4.0        lattice_0.20-35    
[13] glue_1.3.1          digest_0.6.19       promises_1.0.1      XVector_0.22.0     
[17] rvest_0.3.4         colorspace_1.4-1    httpuv_1.5.1        htmltools_0.3.6    
[21] Matrix_1.2-14       plyr_1.8.4          pkgconfig_2.0.2     zlibbioc_1.28.0    
[25] xtable_1.8-4        purrr_0.3.2         scales_1.0.0        webshot_0.5.1      
[29] later_0.8.0         tibble_2.1.1        mgcv_1.8-23         IRanges_2.16.0     
[33] withr_2.1.2         BiocGenerics_0.28.0 lazyeval_0.2.2      mime_0.6           
[37] survival_2.41-3     magrittr_1.5        crayon_1.3.4        evaluate_0.13      
[41] nlme_3.1-137        MASS_7.3-49         xml2_1.2.0          vegan_2.5-5        
[45] tools_3.5.0         hms_0.4.2           stringr_1.4.0       Rhdf5lib_1.4.3     
[49] S4Vectors_0.20.1    munsell_0.5.0       cluster_2.0.7-1     Biostrings_2.50.2  
[53] ade4_1.7-13         compiler_3.5.0      rlang_0.3.4         rhdf5_2.26.2       
[57] grid_3.5.0          iterators_1.0.10    biomformat_1.10.1   rstudioapi_0.10    
[61] htmlwidgets_1.3     crosstalk_1.0.0     igraph_1.2.4.1      labeling_0.3       
[65] rmarkdown_1.12      gtable_0.3.0        codetools_0.2-15    multtest_2.38.0    
[69] reshape2_1.4.3      R6_2.4.0            dplyr_0.8.1         permute_0.9-5      
[73] readr_1.3.1         ape_5.3             stringi_1.4.3       parallel_3.5.0     
[77] Rcpp_1.0.1          tidyselect_0.2.5    xfun_0.7